A predictive model can be applied to data representing a history of events for an entity to compute a value indicative of an outcome related to a reference time for that entity. The effect of an event from an entity's history of events on an outcome for the entity at a reference time can vary based on the type of event and relative time of that event with respect to the reference time. The effect of an event from an entity's history of events on an outcome for the entity also can vary due to other characteristics of the entity in combination with the event. These effects are captured as weights. For an entity, functions of sets of events from the history of events are computed for the entity and a set of weights for events. The computed results are inputs to the predictive model.
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2. The computer system of claim 1, wherein the input reference time is a current time.
A computer system is designed to process time-based data by comparing an input reference time with a stored time value. The system includes a memory storing a time value and a processor configured to receive an input reference time, compare the input reference time with the stored time value, and generate an output based on the comparison. In one configuration, the input reference time is the current time, allowing the system to evaluate temporal relationships between the current time and the stored time value. This enables applications such as time-sensitive decision-making, event triggering, or synchronization tasks where real-time accuracy is critical. The system may further include additional components to adjust the stored time value or modify the comparison logic based on external inputs or predefined rules. The processor may execute algorithms to determine whether the current time meets specific conditions relative to the stored time, such as being earlier, later, or within a defined range. The output can be used to initiate actions, update system states, or provide notifications based on the time comparison results. This approach ensures precise time-based operations in computing environments where synchronization with real-time events is essential.
3. The computer system of claim 1, wherein the input reference time is a time associated with an event.
The invention relates to a computer system for processing time-based data, particularly for handling events and their associated timestamps. The system addresses the challenge of accurately associating events with specific times, ensuring precise temporal data management in applications such as scheduling, logging, or event-driven systems. The computer system includes a processor and memory storing instructions that, when executed, perform operations related to time-based event processing. A key feature is the ability to receive an input reference time, which is a time associated with an event. This reference time can be used to synchronize, log, or analyze events within the system. The system may also include a user interface for displaying or inputting time-related data, enhancing usability for time-sensitive applications. Additionally, the system may generate a visual representation of the event data, such as a timeline or calendar view, to help users interpret temporal relationships. The system can also process multiple events, associating each with its respective reference time and organizing them chronologically or by other criteria. This ensures accurate event tracking and retrieval, improving efficiency in time-dependent workflows. The invention is particularly useful in environments where precise event timing is critical, such as financial transactions, industrial automation, or real-time monitoring systems. By associating events with specific times, the system enables better decision-making and system coordination.
4. The computer system of claim 1, wherein the input reference time is a time for which the outcome of the predicted model is computed.
A computer system is designed to predict outcomes using a machine learning model, addressing the challenge of accurately determining the timing of predictions in dynamic environments. The system processes input data to generate predictions, but a key limitation is ensuring the predicted outcome corresponds to a specific reference time, which is critical for real-time decision-making. The system includes a model training module that trains a machine learning model on historical data to predict future outcomes. A prediction module then applies the trained model to new input data to generate predictions. The system also includes a time alignment module that ensures the predicted outcome is computed for a specified reference time, allowing users to align predictions with real-world events or decision points. This alignment is essential for applications where timing accuracy is critical, such as financial forecasting, logistics, or healthcare monitoring. The system may also include a data preprocessing module to clean and format input data before prediction, and a post-processing module to refine or interpret the predicted outcomes. By integrating time alignment, the system improves the reliability and applicability of predictions in time-sensitive scenarios.
5. The computer system of claim 1, wherein the respective function for a timeline among the plurality of timelines is different from the respective function for at least one other timeline among the plurality of timelines.
This invention relates to a computer system that manages multiple timelines, each with distinct functions. The system addresses the challenge of efficiently organizing and processing diverse temporal data streams, where different timelines serve different purposes. Each timeline in the system is assigned a specific function that differs from at least one other timeline, allowing for specialized handling of data based on its temporal characteristics or application requirements. For example, one timeline may track real-time events, while another may manage historical data or predictive analytics. The system dynamically adjusts the processing and storage of data across these timelines to optimize performance and accuracy. By differentiating the functions of each timeline, the system ensures that data is processed in a manner tailored to its specific use case, improving efficiency and reducing computational overhead. This approach is particularly useful in applications requiring simultaneous management of multiple time-sensitive data streams, such as financial trading, logistics, or IoT sensor networks. The system may include mechanisms to synchronize or correlate data across timelines when necessary, ensuring consistency and coherence in the overall data model. The invention enhances flexibility and scalability in handling complex temporal data scenarios.
6. The computer system of claim 1, wherein the function is a linear function.
A computer system is designed to process data using a mathematical function, specifically a linear function. The system includes a processor and memory storing instructions that, when executed, cause the processor to perform operations. These operations involve receiving input data, applying a linear function to the input data, and generating output data based on the application of the linear function. The linear function is defined by a set of coefficients that determine the relationship between the input data and the output data. The system may also include additional components, such as input and output interfaces, to facilitate data processing. The use of a linear function ensures that the relationship between the input and output data is predictable and computationally efficient, making the system suitable for applications requiring fast and accurate data transformations. The system may be part of a larger computational framework, where the linear function is one of several operations performed to achieve a specific computational goal. The linear function can be applied to various types of data, including numerical values, vectors, or matrices, depending on the requirements of the application. The system is designed to handle large datasets efficiently, ensuring that the linear function is applied in a manner that minimizes computational overhead while maintaining accuracy.
7. The computer system of claim 1, wherein the function is a non-linear function.
A computer system is designed to process data using a non-linear function to enhance computational efficiency or accuracy. The system includes a processing unit configured to execute a function on input data, where the function is specifically a non-linear function. Non-linear functions are used to model complex relationships in data that cannot be effectively captured by linear methods. This approach is particularly useful in applications such as machine learning, signal processing, and optimization, where linear approximations may fail to capture the underlying patterns. The non-linear function may include operations like polynomial transformations, exponential functions, or neural network activations, which introduce non-linearity into the processing pipeline. By applying such functions, the system can handle non-linear data relationships, improving performance in tasks like classification, regression, and feature extraction. The use of non-linear functions allows the system to adapt to diverse data distributions and improve accuracy in predictive modeling. This method is distinct from linear processing techniques, which are limited to straightforward, additive relationships. The system may be integrated into larger computational frameworks, such as deep learning models or signal processing pipelines, to enhance their capabilities. The non-linear function is applied to input data, transforming it into a form that better represents the underlying data structure, leading to more accurate and efficient computations.
8. The computer system of claim 1, wherein each unique tuple in the set of weights has a single weight.
A computer system is designed to optimize the processing of data tuples, particularly in applications requiring efficient storage and retrieval of weighted values. The system addresses the challenge of managing large datasets where each data tuple may be associated with multiple weights, leading to redundancy and inefficiency in storage and computation. The invention improves upon prior systems by ensuring that each unique tuple in a set of weights is assigned a single weight, eliminating redundancy and enhancing performance. The system includes a data storage component that stores tuples and their associated weights. A processing module generates or receives a set of weights, where each weight corresponds to a unique tuple. The system ensures that no duplicate weights are assigned to the same tuple, thereby optimizing memory usage and computational efficiency. This is particularly useful in applications such as machine learning, data analytics, and database management, where minimizing redundancy and maximizing processing speed are critical. The system may also include a validation module to verify that each tuple in the set of weights is unique and that only one weight is assigned per tuple. This ensures data integrity and prevents errors in subsequent processing steps. The invention can be implemented in various computing environments, including cloud-based systems, distributed computing networks, and embedded systems, depending on the specific application requirements. By reducing redundancy and improving data handling efficiency, the system enhances overall performance and scalability.
9. The computer system of claim 1, wherein at least one tuple in the set of weights has a plurality of weights, and the calculation module selects from among the plurality of weights.
This invention relates to a computer system for processing data using weighted tuples, addressing the challenge of efficiently managing and selecting weights in computational models. The system includes a storage module that holds a set of weights, where each weight is associated with a tuple in a data structure. At least one tuple in the set contains multiple weights, allowing for flexibility in weight selection. A calculation module dynamically selects from these multiple weights based on predefined criteria or real-time conditions, optimizing performance or accuracy in computations. The system may also include a training module that adjusts the weights during a training phase, ensuring adaptability to new data or changing requirements. The selection process may involve evaluating factors such as data context, computational efficiency, or model accuracy to determine the most appropriate weight for a given operation. This approach enhances the system's ability to handle complex data relationships and improve decision-making in applications like machine learning, data analysis, or optimization tasks. The invention provides a scalable and efficient way to manage and utilize multiple weights per tuple, improving the adaptability and performance of computational models.
10. The computer system of claim 1, wherein the entity comprises a patient, and the entity profile characteristic comprises at least one of age, a comorbidity, a behavior, a characteristic from a family history, or genetic profile attribute of the patient.
This invention relates to a computer system for managing and analyzing entity profiles, particularly in healthcare applications. The system is designed to process and utilize detailed patient data to improve medical decision-making, treatment planning, or risk assessment. The system includes a database storing entity profiles, where each profile contains specific characteristics relevant to the entity, such as a patient. These characteristics may include age, comorbidities, behaviors, family history, or genetic profile attributes. The system is configured to retrieve, analyze, and apply these characteristics to generate insights or recommendations tailored to the patient's unique profile. By integrating multiple data points, the system enhances personalized healthcare by identifying risk factors, predicting outcomes, or suggesting interventions based on the patient's specific attributes. The invention aims to improve diagnostic accuracy, treatment effectiveness, and preventive care by leveraging comprehensive patient data in a structured and automated manner. The system may also support research by enabling large-scale analysis of patient profiles to uncover trends or correlations in health conditions.
11. The computer system of claim 1, wherein the set of weights comprises a plurality of weight tables, including a first weight table for a first outcome and a second weight table for a second outcome different from the first outcome, wherein a first predictive model generates values indicative of the first outcome using the first weight table, and a second predictive model generates values indicative of the second outcome using the second weight table.
A computer system is designed to improve predictive modeling by using multiple weight tables for different outcomes. The system addresses the challenge of accurately predicting multiple distinct outcomes from a single dataset, where traditional models may struggle due to shared weights or insufficient specialization. The system includes a set of weights organized into multiple weight tables, each corresponding to a different outcome. For example, a first weight table is used to generate predictions for a first outcome, while a second weight table is used for a second, distinct outcome. Separate predictive models are applied to these tables, allowing each model to specialize in its respective outcome. This approach enhances prediction accuracy by avoiding the limitations of a single, generalized model. The system may also include additional components, such as data processing modules and training mechanisms, to refine the weight tables over time. By leveraging specialized weight tables, the system provides more precise and reliable predictions for multiple outcomes compared to conventional methods.
12. The computer system of claim 1, wherein the set of weights comprises a weight table corresponding to a first outcome, and wherein the predictive model outputs a value indicative of a second outcome different from the first outcome.
A computer system is described for predictive modeling, addressing the challenge of accurately forecasting outcomes when the model's output differs from the target outcome of interest. The system includes a predictive model that generates a value representing a second outcome, which is distinct from a first outcome associated with a predefined weight table. The weight table is used to adjust or interpret the model's output to align with the first outcome, enabling more accurate predictions or decision-making. The system leverages the relationship between the first and second outcomes to improve predictive performance, particularly in scenarios where direct measurement or modeling of the first outcome is impractical or inefficient. This approach allows for flexible adaptation of predictive models to different types of outcomes while maintaining accuracy and reliability. The system may be applied in various domains, such as finance, healthcare, or operations, where outcome prediction is critical.
13. The computer system of claim 12, wherein the second outcome is correlated with the first outcome.
This invention relates to computer systems designed to analyze and correlate outcomes from different processes or events. The system includes a processing unit configured to generate a first outcome based on input data and a second outcome based on additional input data. The key feature is that the second outcome is correlated with the first outcome, meaning the system establishes a relationship or dependency between the two outcomes. This correlation may involve statistical analysis, pattern recognition, or other computational techniques to determine how the second outcome is influenced by or related to the first outcome. The system may also include memory storage for retaining the outcomes and correlation results, as well as input/output interfaces for receiving data and outputting the correlated results. The correlation process may be used in various applications, such as predictive modeling, decision-making systems, or data-driven analytics, where understanding the relationship between different outcomes is critical. The system ensures that the correlation is accurately computed and can be used to improve the reliability and accuracy of subsequent analyses or decisions.
14. The computer system of claim 1, wherein the set of weights comprises a plurality of weight tables, wherein the calculation module accesses the plurality of weight tables to compute the results provided as inputs to the predictive model.
This invention relates to a computer system for predictive modeling, specifically addressing the challenge of efficiently managing and applying weights in predictive models. The system includes a calculation module that processes inputs to generate results, which are then used as inputs to a predictive model. A key feature is the use of a set of weights, structured as multiple weight tables, to enhance the accuracy and flexibility of the predictive model. The calculation module accesses these weight tables to compute the results, allowing for dynamic adjustments and improved performance. The weight tables may be organized to optimize storage, retrieval, and computation, ensuring efficient model training and inference. This approach enables the system to handle complex relationships between input features and model outputs while maintaining computational efficiency. The invention is particularly useful in applications requiring real-time predictions, such as financial forecasting, risk assessment, or recommendation systems, where accurate and timely results are critical. By leveraging multiple weight tables, the system can adapt to varying data distributions and improve model generalization. The overall design focuses on balancing computational efficiency with model accuracy, making it suitable for deployment in resource-constrained environments.
15. The computer system of claim 1, wherein the predictive model generates a value indicative of a first outcome for an entity, wherein the first outcome is correlated to a second outcome, and the computer system reports a value indicative of the second outcome for the entity based on the value indicative of the first outcome for the entity.
A computer system uses a predictive model to generate a value representing a first outcome for an entity, such as a user, device, or process. The first outcome is correlated to a second outcome, meaning the first outcome can be used to infer or estimate the second outcome. The system then reports a value indicative of the second outcome based on the value of the first outcome. This approach allows the system to predict or estimate outcomes that may be difficult to measure directly by leveraging a related, more accessible outcome. The predictive model may be trained using historical data to establish the correlation between the first and second outcomes. The system can be applied in various domains, such as finance, healthcare, or industrial processes, where direct measurement of certain outcomes is impractical or costly. By using the first outcome as a proxy, the system provides actionable insights or predictions about the second outcome, improving decision-making or automation in the relevant domain.
16. The computer system of claim 1, wherein a plurality of types of events are grouped together as a medical instance, and wherein at least one weight in the set of weights is associated with the medical instance.
This invention relates to a computer system for processing medical data, specifically addressing the challenge of efficiently analyzing and interpreting diverse medical events. The system groups multiple types of medical events into a single medical instance, allowing for streamlined data processing. Each medical instance is assigned at least one weight from a predefined set of weights, which helps prioritize or categorize the grouped events. The system may also include a neural network trained to process these weighted medical instances, enabling accurate predictions or classifications based on the aggregated data. The neural network may be trained using a loss function that incorporates the weights, ensuring that the model effectively learns from the structured medical instances. Additionally, the system may generate a prediction for a target medical event by processing the medical instance through the neural network, providing actionable insights for healthcare applications. The grouping of events and weighted processing improves efficiency and accuracy in medical data analysis, particularly in scenarios where multiple related events contribute to a diagnosis or treatment decision.
17. The computer system of claim 1, wherein the set of weights comprises a plurality of different weights for a type of event for different combinations of that type of event with different relative times.
This invention relates to a computer system for analyzing event data, particularly for processing events with varying significance based on their timing relative to other events. The system addresses the challenge of accurately assessing the impact of events in sequences where the same type of event may have different importance depending on when it occurs relative to other events. For example, a user action like a click may be more significant if it follows a specific sequence of prior actions within a short time window, whereas the same click occurring in isolation may have less importance. The system includes a set of weights assigned to different types of events, where each weight is adjusted based on the relative timing of the event in relation to other events. Specifically, the set of weights contains multiple values for a given event type, with each value corresponding to a different combination of that event type and its relative time to other events. This allows the system to dynamically adjust the influence of an event in a sequence, improving the accuracy of event-based predictions or analyses. The system processes event data by applying these time-dependent weights to compute a weighted representation of the event sequence, which can then be used for tasks such as anomaly detection, user behavior modeling, or predictive analytics. The approach ensures that temporal relationships between events are properly accounted for, enhancing the system's ability to interpret complex event patterns.
18. The computer system of claim 1, wherein for a first tuple having a first weight for a first combination of a first type of event and a first relative time, and a second tuple having a second weight for a second combination of the first type of event and a second relative time longer than the first relative time, the first weight is less than the second weight.
This invention relates to a computer system for analyzing event sequences, particularly focusing on the temporal relationships between events of the same type. The system addresses the challenge of accurately modeling the significance of events based on their timing relative to other events in a sequence. In prior systems, events of the same type may be treated uniformly, regardless of their temporal context, leading to inaccurate predictions or analyses. The system includes a data structure storing tuples, where each tuple associates an event type, a relative time, and a weight. The weight represents the importance or impact of an event of a given type occurring at a specific relative time. For example, if two events of the same type occur in a sequence, the system assigns a higher weight to the event that occurs later in time. Specifically, if a first tuple defines a first weight for an event type at a first relative time, and a second tuple defines a second weight for the same event type at a second relative time (longer than the first), the second weight will be greater than the first. This ensures that temporally later events of the same type are given greater significance in the analysis. The system dynamically adjusts weights based on temporal context, improving the accuracy of event sequence modeling in applications such as anomaly detection, predictive analytics, or behavioral analysis.
19. The computer system of claim 18, wherein for a third tuple having a third weight for a third combination of a second type of event and a third relative time, and a fourth tuple having a fourth weight for a fourth combination of the second type of event and a fourth relative time longer than the third relative time, the third weight is greater than the fourth weight.
This invention relates to a computer system for analyzing event sequences, particularly focusing on the temporal relationships between different types of events. The system addresses the challenge of accurately modeling the significance of events based on their timing relative to other events, which is critical in applications such as predictive analytics, anomaly detection, and behavioral analysis. The system processes event data by storing tuples that represent combinations of event types and their relative times. Each tuple is assigned a weight that quantifies the importance or impact of the event combination. For a given type of event, the system ensures that the weight of a tuple with a shorter relative time is greater than the weight of a tuple with a longer relative time. This means that events occurring closer in time to a reference event are considered more significant than those occurring farther away. The system dynamically adjusts these weights to reflect the varying influence of events based on their temporal proximity, improving the accuracy of event sequence analysis. The invention enhances the ability to detect patterns, predict outcomes, and identify anomalies by prioritizing temporally proximate events, making it valuable in fields such as cybersecurity, financial fraud detection, and user behavior modeling.
20. The computer system of claim 1, wherein the result output for an entity by the predictive model is a value indicative of a probability the entity has the outcome.
This invention relates to a computer system for predicting outcomes using machine learning models. The system addresses the challenge of accurately assessing the likelihood of specific outcomes for entities, such as individuals or objects, by leveraging predictive modeling techniques. The system includes a data processing module that receives input data associated with an entity and a predictive model trained to analyze the input data. The model generates a result output representing a probability that the entity will exhibit a particular outcome. The system further includes a user interface for displaying the probability result, allowing users to interpret the likelihood of the outcome. The predictive model is trained using historical data, where the training process involves adjusting model parameters to minimize prediction errors. The system may also include a data preprocessing module to clean, normalize, or transform input data before analysis. The probability output can be used for decision-making, risk assessment, or other applications where outcome prediction is valuable. The invention improves upon existing systems by providing a probabilistic assessment rather than a binary classification, offering more nuanced insights into the likelihood of outcomes.
21. The computer system of claim 1, wherein the result output for an entity by the predictive model is a value from a set of discrete values.
The invention relates to a computer system that uses predictive models to generate outputs for entities, where the outputs are discrete values from a predefined set. The system is designed to address the challenge of accurately predicting outcomes or classifications for entities, such as users, objects, or processes, where the results must fall within a finite set of possible values. The predictive model processes input data associated with an entity and produces a result that is one of the discrete values in the set, enabling decision-making or further processing based on the predicted outcome. The system may include components for data preprocessing, model training, and result interpretation to ensure the predictions are reliable and actionable. The discrete nature of the output allows for clear categorization or classification, which is useful in applications like risk assessment, recommendation systems, or quality control, where distinct outcomes are required. The invention improves upon existing systems by providing structured, interpretable results that can be directly applied in decision-making workflows.
22. The computer system of claim 21, wherein the discrete range of values comprises a finite set of integers comprising at least 1 to 1000.
A computer system is designed to process and analyze data within a discrete range of values, specifically a finite set of integers from 1 to 1000. This system addresses the challenge of efficiently handling and interpreting data that falls within a predefined, bounded numerical range, ensuring accuracy and reliability in computational tasks. The system includes components for inputting, storing, and processing these integer values, with mechanisms to validate and manage the data to prevent errors or inconsistencies. The discrete range ensures that all values are within a controlled scope, reducing the risk of overflow or underflow errors and simplifying data validation processes. The system may also include algorithms for sorting, filtering, or performing mathematical operations on the integer values, optimizing performance for applications requiring precise numerical analysis. By restricting the range to integers from 1 to 1000, the system ensures compatibility with applications that require finite, bounded datasets, such as inventory management, statistical analysis, or numerical simulations. The system may further integrate with other computational tools or databases to enhance functionality and scalability.
23. The computer system of claim 1, wherein the result output for an entity by the predictive model is a value from a range of continuous values.
The invention relates to a computer system that uses predictive models to generate outputs for entities, where the output is a continuous value within a defined range. The system is designed to address the challenge of accurately predicting outcomes or characteristics for entities, such as individuals, objects, or processes, where the result is not binary or categorical but instead varies continuously. The predictive model processes input data related to the entity and produces a numerical output that falls within a specified range, allowing for fine-grained predictions. This approach enables more precise decision-making compared to systems that only provide discrete or binary results. The system may include preprocessing steps to prepare input data, model training to optimize prediction accuracy, and post-processing to refine or interpret the continuous output. The continuous value output can be used in various applications, such as risk assessment, performance evaluation, or resource allocation, where nuanced predictions are beneficial. The invention ensures that the predictive model is capable of handling and outputting continuous values, enhancing its utility in scenarios requiring detailed or probabilistic predictions.
24. The computer system of claim 1, wherein the result output for an entity by the predictive model is a value from a scale that ranks or categorizes entities with respect to the outcome.
The invention relates to a computer system that uses predictive modeling to analyze entities and generate ranked or categorized outputs. The system addresses the challenge of evaluating entities (such as individuals, objects, or processes) to predict an outcome of interest, such as risk, performance, or suitability. The predictive model processes input data associated with each entity and produces a result that places the entity on a predefined scale. This scale ranks or categorizes entities relative to the outcome, enabling comparisons and decision-making. The system may incorporate additional features, such as data preprocessing, model training, or real-time analysis, to enhance accuracy and usability. The ranked or categorized output allows users to assess entities objectively, improving efficiency in applications like risk assessment, resource allocation, or quality control. The invention ensures that the predictive model's output is interpretable and actionable, facilitating better decision-making in domains where entity evaluation is critical.
25. The computer system of claim 24, wherein the scale comprises a finite set of integers comprising at least 1 to 1000.
The invention relates to a computer system for managing and analyzing data using a scalable measurement system. The system addresses the challenge of efficiently quantifying and comparing diverse data types, such as user feedback, performance metrics, or sensor readings, by providing a standardized scale. The scale is defined as a finite set of integers ranging from at least 1 to 1000, allowing precise and granular measurements. The system processes input data, maps it to the defined scale, and performs computations or comparisons based on the scaled values. This enables consistent evaluation across different data sources, improving decision-making and analysis accuracy. The system may also include additional features, such as dynamic scaling adjustments or normalization, to adapt to varying data ranges while maintaining the finite integer scale. The invention ensures compatibility with existing data processing workflows by integrating the scaled measurements into broader analytical frameworks. The finite integer range prevents overflow or underflow issues while providing sufficient resolution for detailed analysis. This approach enhances data interoperability and simplifies comparisons across heterogeneous datasets.
26. The computer system of claim 1, wherein the result output for an entity by the predictive model is a value indicative of a probability the entity has the outcome.
This invention relates to a computer system that uses predictive modeling to assess the likelihood of a specific outcome for an entity. The system addresses the challenge of accurately predicting outcomes in domains such as finance, healthcare, or risk assessment, where probabilistic predictions are valuable for decision-making. The system includes a predictive model trained on historical data to generate a result output for an entity, where the result is a numerical value representing the probability that the entity will experience the specified outcome. The model processes input data associated with the entity, such as features or attributes, to produce this probabilistic output. The system may also include components for data preprocessing, model training, and result interpretation to ensure the predictions are reliable and actionable. The probabilistic output allows users to quantify uncertainty and make informed decisions based on the predicted likelihood of the outcome. This approach is particularly useful in scenarios where binary or categorical predictions are insufficient, and a continuous probability measure is needed for risk assessment, resource allocation, or personalized recommendations. The system may be integrated into larger decision-support frameworks or deployed as a standalone tool for probabilistic forecasting.
27. The computer system of claim 1, wherein the result output for an entity by the predictive model is a value indicative of an estimation of risk that the entity has the outcome.
This invention relates to a computer system for predicting outcomes using a predictive model, specifically estimating the risk that an entity will experience a particular outcome. The system processes input data associated with the entity, applies a predictive model to generate a result, and outputs a value representing the estimated risk of the outcome occurring. The predictive model is trained on historical data to learn patterns and relationships between input features and the outcome of interest. The system may include preprocessing steps to prepare the input data, such as normalization, feature selection, or transformation, to improve model performance. The output risk value can be used for decision-making, such as risk assessment, fraud detection, or resource allocation. The system may also include mechanisms to update the predictive model with new data to maintain accuracy over time. The invention addresses the challenge of accurately predicting outcomes in scenarios where historical data is available but the underlying relationships are complex or non-linear. The risk estimation output provides a quantifiable measure that can be interpreted and acted upon by users or integrated into automated decision-making processes. The system may be applied in various domains, including finance, healthcare, or insurance, where outcome prediction is critical.
28. The computer system of claim 1, wherein the plurality of categories of events include at least two of procedures codes, medication codes, or diagnosis codes.
This invention relates to a computer system for processing and categorizing medical or healthcare-related events. The system addresses the challenge of efficiently organizing and analyzing large volumes of healthcare data, which often includes diverse types of events such as procedures, medications, and diagnoses. The system categorizes these events into predefined groups based on their type, such as procedure codes, medication codes, or diagnosis codes. This categorization allows for improved data management, reporting, and analysis, enabling healthcare providers and administrators to better track and evaluate patient care, treatment outcomes, and resource utilization. The system may also support additional functionalities like data validation, trend analysis, and integration with electronic health records (EHRs) or billing systems. By organizing events into structured categories, the system enhances data accuracy, accessibility, and usability, ultimately supporting better decision-making in healthcare settings.
29. The computer system of claim 1, wherein the relative time for events in the set of weights is computed in units of months.
The invention relates to a computer system for analyzing and processing event data, specifically focusing on the temporal relationships between events. The system addresses the challenge of accurately representing and computing the relative time between events in a dataset, particularly when these events are associated with weights that influence their significance or impact. A key aspect of the system is the ability to compute the relative time for events in a set of weights, with the time being measured in units of months. This allows for precise temporal analysis over extended periods, which is critical for applications such as financial forecasting, historical trend analysis, or long-term project planning. The system likely includes components for data input, event weighting, and time computation, ensuring that the temporal relationships between events are accurately captured and utilized in subsequent processing steps. By standardizing the time measurement in months, the system simplifies comparisons and calculations across different datasets or timeframes, enhancing the reliability and consistency of the analysis. The invention is particularly useful in scenarios where events occur over months or years, and their relative timing is a critical factor in decision-making or predictive modeling.
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April 16, 2019
April 23, 2024
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